Medical Image Authentication through Watermarking ... · Medical Image Authentication through Watermarking Preserving ROI Dissertation submitted in partial ful llment of the requirements

Medical Image Authentication through

Watermarking Preserving ROI

Dissertation

submitted in partial fulfillment of the requirements

for the degree of

Master of Technology, Computer Engineering

by

Sonika C. Rathi

Roll No: 121022005

under the guidance of

Prof. V. S. Inamdar

Department of Computer Engineering and Information Technology

College of Engineering, Pune

Pune - 411005.

June 2012

Dedicated to

my mother

Smt. Kavita C. Rathi

and

my father

Shri. Chandrakant D. Rathi

DEPARTMENT OF COMPUTER ENGINEERING AND

INFORMATION TECHNOLOGY,

COLLEGE OF ENGINEERING, PUNE

CERTIFICATE

This is to certify that the dissertation titled

Medical Image Authentication throughWatermarking Preserving ROI

has been successfully completed

By

Sonika C. Rathi

(121022005)

and is approved for the degree of

Master of Technology, Computer Engineering.

Prof. V. S. Inamdar, Dr. Jibi Abraham,

Guide, Head,

Department of Computer Engineering Department of Computer Engineering

and Information Technology, and Information Technology,

College of Engineering, Pune, College of Engineering, Pune,

Shivaji Nagar, Pune-411005. Shivaji Nagar, Pune-411005.

Date :

Abstract

Telemedicine is a well-known application, where enormous amount of medical data

need to be securely transferred over the public network and manipulate effectively.

Medical image watermarking is an appropriate method used for enhancing security

and authentication of medical data, which is crucial and used for further diagnosis

and reference. This project focuses on the study of medical image watermarking

methods for protecting and authenticating medical data. Additionally, it covers

algorithm for application of water marking technique on Region of Non Interest

(RONI) of the medical image preserving Region of Interest (ROI).

The medical images can be transferred securely by embedding watermarks in

RONI allowing verification of the legitimate changes at the receiving end without

affecting ROI. Segmentation plays an important role in medical image processing

for separating the ROI from medical image. The proposed system separate the

ROI from medical image by GUI based approach, which works for all types of

medical images. The experimental results show the satisfactory performance of

the system to authenticate the medical images preserving ROI.

iii

Acknowledgements

I express my sincere gratitude towards my guide Prof. V. S. Inamdar for her

constant help, encouragement and inspiration throughout the project work. Also

I would like to thank our Head of Department, Prof. Jibi Abraham for her able

guidance and for providing all the necessary facilities, which were indispensable

in the completion of this project.

I take this opportunity to express my hearty thanks to all those who helped me

in the completion of my project work. I am very grateful to the authors of various

articles on the Internet, for helping me become aware of the research currently

ongoing in this field.

I am very thankful to my parent for their constant support. I would also

like to thank Manisha Mantri, Dr. Dinesh Mantri and Pravin Reddy

for their valuable suggestions and helpful discussions. Last, but not the least, I

would like to thank my classmates for their valuable comments, suggestions and

unconditional support.

Sonika C. Rathi

College of Engineering, Pune

May 21, 2012

iv

Contents

Abstract iii

Acknowledgements iv

List of Figures viii

1 Introduction: Digital Watermarking 1

1.1 Digital watermarking . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Principle of Digital Watermarking . . . . . . . . . . . . . . . . . . . 2

1.3 Types of Watermarking System . . . . . . . . . . . . . . . . . . . . 4

1.3.1 Visible watermarking system . . . . . . . . . . . . . . . . . . 4

1.3.2 Invisible watermarking system . . . . . . . . . . . . . . . . . 5

1.3.3 Blind watermarking system . . . . . . . . . . . . . . . . . . 5

1.3.4 Non-blind watermarking system . . . . . . . . . . . . . . . . 5

1.3.5 Robust watermarking system . . . . . . . . . . . . . . . . . 5

1.3.6 Fragile watermarking system . . . . . . . . . . . . . . . . . . 6

1.4 Properties of Digital Watermarking . . . . . . . . . . . . . . . . . . 6

1.4.1 Imperceptibility . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.4.2 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.4.3 Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.4.4 Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.4.5 Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.4.6 Invertibility . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.4.7 Verification . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.5 Watermarking Techniques . . . . . . . . . . . . . . . . . . . . . . . 8

1.5.1 Spatial domain watermarking . . . . . . . . . . . . . . . . . 9

1.5.2 Frequency domain watermarking . . . . . . . . . . . . . . . 10

1.6 Application of Watermarking . . . . . . . . . . . . . . . . . . . . . 12

1.7 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2 Medical Image Watermarking Introduction 14

2.1 Principle of Medical Image Watermarking . . . . . . . . . . . . . . 16

2.2 Requirements of Medical Image Watermarking . . . . . . . . . . . . 17

2.2.1 Imperceptibility . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.2.2 Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.2.3 Authenticity . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.2.4 Reversibility . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.2.5 Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.2.6 Intactness of ROI . . . . . . . . . . . . . . . . . . . . . . . . 18

3 Literature Survey 19

3.1 Region of Interest (ROI) Segmentation . . . . . . . . . . . . . . . . 19

3.1.1 Magnetic Resonance Imaging (MRI) . . . . . . . . . . . . . 20

3.1.2 Computed Tomography (CT) . . . . . . . . . . . . . . . . . 24

3.2 Medical Image Watermarking . . . . . . . . . . . . . . . . . . . . . 27

4 Proposed System for Medical Image Watermarking preserving

ROI 30

4.1 Separating ROI from medical image . . . . . . . . . . . . . . . . . . 31

4.2 Medical Image Watermarking System . . . . . . . . . . . . . . . . . 33

4.2.1 Integer to Integer transform . . . . . . . . . . . . . . . . . . 34

4.3 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

4.3.1 Description . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

4.3.2 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

5 Experiments and Results 41

5.1 The experiments and results of the system without attacks . . . . . 41

5.1.1 CT Scan Images . . . . . . . . . . . . . . . . . . . . . . . . 41

5.1.2 MRI Images . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

5.1.3 X-Ray Images . . . . . . . . . . . . . . . . . . . . . . . . . . 44

5.1.4 Ultrasound Images . . . . . . . . . . . . . . . . . . . . . . . 46

5.2 Embedded and extracted watermark with attacks . . . . . . . . . . 46

6 Conclusion and Future Work 53

6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Bibliography 55

vi

List of Figures

1.1 A Typical Watermarking System . . . . . . . . . . . . . . . . . . . 3

1.2 Types of Watermarking Techniques . . . . . . . . . . . . . . . . . . 9

1.3 The filter bank structure used in wavelet decomposition of an image 11

2.1 A typical e-diagnosis Model . . . . . . . . . . . . . . . . . . . . . . 15

2.2 Block diagram of Medical Image watermarking . . . . . . . . . . . . 16

3.1 Medical image indicating ROI . . . . . . . . . . . . . . . . . . . . . 20

4.1 Medical Image Watermarking Approach Preserving ROI . . . . . . 31

4.2 Interface for GUI based approach . . . . . . . . . . . . . . . . . . . 32

4.3 Sub-band structure a of 4-level wavelet transform . . . . . . . . . . 36

4.4 Ultrasound Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

4.5 Quantization Procedure . . . . . . . . . . . . . . . . . . . . . . . . 38

5.1 Segmenatted ROI of host image . . . . . . . . . . . . . . . . . . . . 42

5.2 (a) The original host CT scan image, (b)Roi removed image,(c)Emebedded

image without ROI, (d)Final embedded image with ROI . . . . . . 42

5.3 Recovered original image . . . . . . . . . . . . . . . . . . . . . . . . 42

5.4 Embedded and extracted watermark values without any attacks . . 43

5.5 (a) The original host MRI image, (b)ROI image of host image,(c)Roi

removed image (c)Emebedded image without ROI, (d)Final embed-

ded image with ROI . . . . . . . . . . . . . . . . . . . . . . . . . . 44

5.6 Embedded and extracted watermark values without any attacks for

MRI image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

5.7 (a) The original host X-Ray image, (b)ROI image of host image,(c)Roi

removed image (c)Emebedded image without ROI, (d)Final embed-

ded image with ROI . . . . . . . . . . . . . . . . . . . . . . . . . . 45


X-Ray image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

5.9 (a) The original host Ultrasound image, (b)ROI image of host

image,(c)Roi removed image (c)Emebedded image without ROI,

(d)Final embedded image with ROI . . . . . . . . . . . . . . . . . . 46


Ultrasound image . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

5.11 (a) The original watermarked CT scan image, (b)The image after

sharpning attack with 0.02 factor . . . . . . . . . . . . . . . . . . . 48

5.12 Embedded and extracted watermark values with sharpning attck

(0.02 factor) image . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

5.13 (a) The original watermarked MRI image, (b)The image after His-

togram attack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

5.14 Embedded and extracted watermark values after histogram attack

on image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

5.15 (a) The original watermarked X-Ray image, (b)The image after

10% JEPG compression attack . . . . . . . . . . . . . . . . . . . . . 50

5.16 Embedded and extracted watermark values after JEPG Compres-

sion attack on X-Ray image . . . . . . . . . . . . . . . . . . . . . . 50

5.17 (a) The original watermarked Ultrasound image, (b)The image after

up and down sampling attack . . . . . . . . . . . . . . . . . . . . . 51

5.18 Embedded and extracted watermark values after down and up sam-

pling attack on Ultrasound image . . . . . . . . . . . . . . . . . . . 52

viii

Chapter 1

Introduction: Digital

Watermarking

1.1 Digital watermarking

In recent year all the business applications are moving towards the digital era,

because of great development in latest technologies such as in the area of com-

munication, networked multimedia system, digital data storage etc. Also from

the last two decades use of internet is rapidly increased in business environment

towards achievement of effectiveness, convenience and Security by introducing the

digitization in their work.

It was estimated that in 1993 the Internet will carry only 1% of the informa-

tion however by 2000 this figure had grown to 51%, and by 2007 more than 97 %

information was carried away across the globe. A study conducted by JupiterRe-

search says that 1.1 billion people have regular Web access and use application like

electronic mail, instant messaging, social networking, online messaging etc. which,

helps in growth & knowledge sharing in different domains such as education, re-

search, development, Medical, and many business etc. In business applications to

speed up the business process communication use of digital media has been drasti-

cally increased. This digital data includes text, images, audio, video and software

which are transferred over open public network, hence there is need to protect this

data. There are many techniques that are available for protection of this digital

data, such as encryption (cryptography), authentication and time stamping. Also

there is another method that improved the protection of digital data by merging

a low level signal directly into the digital data. This low level signal is known as

watermark, that uniquely identifies the ownership and provide the security to the

1

1.2 Principle of Digital Watermarking

digital data and can be easily extracted.

The process of embedding the watermark into a digital data is known as Dig-

ital Watermarking. It is a process of embedding unremarkable logos or labels or

information data or pattern into the digital data [1]. The concept of digital wa-

termarking is associated with the stegnography. It is defined as covered writing,

which hides the important message in a covered media while, digital watermark-

ing is a way of hiding a secret or personal message to provide copyrights and the

data integrity. Digital image watermarking is a new approach, which is suitable

for medical, military, and archival based applications. The embedded watermarks

are difficult to remove and typically imperceptible, could be in the form of text,

image, audio, or video.

The embedding of secret watermark in digital data, no matter how much invis-

ible it may be. However it leads to some degradation in the resultant embedded

digital data. To overcome this and to retrieve the original data, reversible wa-

termarking has been implemented, which considered as a best approach over the

cryptography. In cryptography after encryption the resultant data may not be

visible or understandable also at the time of retrieval this may lead to loss of se-

mantic information of host data, which is not in case of watermarking. In digital

data several watermarks can be embedded at the same time and this is known as

multiple watermarking technique. A digital watermark also considered as digital

signature which provides the authenticity. A given watermark may be unique to

each copy (e.g. to identify the intended recipient), or be common to multiple

copies (e.g. to identify the document source).


Basically, digital watermarking is consisted of two main processes, namely embed-

ding process and extracting process. During the embedding process, watermark

is embedded into the multimedia data (digital data). The original digital data

(multimedia content) will slightly modified after embedding the watermark, this

modified data is called as watermarked data. While in extraction process this

embedded watermark is extracted from the watermarked data and recovers the

original multimedia data. The extracted watermark is then compared with the

original watermark; if the watermark is same it results in authenticated data.

During the transmission of the watermarked data over the public network at-

2


tacker may tamper the data, and if any modification in the data can be detected

by comparing the extracted watermark with the original watermark.

Figure 1.1: A Typical Watermarking System

A typical watermarking system is shown in Figure 1.1 which includes water-

mark embedder and watermark extractor. The inputs to the embedder are multi-

media data and watermark, which is to be embedded into the original multimedia

data. The output of watermark embedder is watermarked data (watermarked

content). The inputs to the watermark extractor depending on the method are

original multimedia or original watermark. The watermark extraction process in-

volves two steps [2]. In the first step one or more pre-process is applied on the

watermarked data to extract a vector called extracted watermark. Then the sec-

ond step is to determine whether the extracted watermark is same as original

watermark by comparing the extracted watermark with the original watermark

called reference watermark. The result of second step is to measure the confidence

by indicating how likely the original watermark is present in the digital data [3].

The multimedia data in Figure 1.1 includes text [4], image [5], audio file [6], video

[7], 3D data [8, 9], and object [10].

Suppose that X is the original multimedia data and W is the watermark to

be embed. In digital watermarking system a embedding function E(.) takes X

and W as a input values and gives X ′ i.e. watermarked data as a output. X ′ is

3

1.3 Types of Watermarking System

obtained as:

X ′ = p (X,W ) (1.1)

The embedding algorithm is considered as robust if watermark is embedded in a

way such that it can survive even if the watermarked data X’ goes through several

attacks. During extraction process the extraction function D(.) is defined as:

W ′ = D (X ′, [X], [W ]) (1.2)

Where W’ is retrieved watermark, X and W enclosed in braces [ ]can be optional

inputs for extraction function, which depends on the application. For example

[X] is used when the watermarking system is non-blind, this system is suitable for

the application where to extract the watermark original image is needed. If the

watermarking system is blind the input to the extraction function is [W] only.

A typical watermarking should satisfy the following requirements.

• The watermark W should be extracted from X’ with or without X

• X’ should be as close to X as possible

• If X’ is not manipulated/modified, the extracted watermark should be same

as W

• For robust watermarking, if X’ is modified, W’ should still match W to give

clear judgment of the existence of watermark

• For fragile watermarking, after even the slight manipulation to X’ extracted

W’ should be totally different from W. In such system W indicates the

tampering to the X’


Depending on the application, watermarking system can be of different types.

1.3.1 Visible watermarking system

In visible watermarking system watermark (text or image) is semi-transparently

embedded into original data. Visible watermarking is more robust against image

4


transformation attacks, which provides copyrights protection of intellectual prop-

erty thats in digital format. In visible watermarking watermarked data is view as

digitally stamped document.

1.3.2 Invisible watermarking system

In invisible watermarking system watermark is embedded into the original data

in such a way that the embedded watermark should not be visible by naked eyes.

Only electronic devices (or specialized software) can extract the embedded in-

formation to prove the authenticity. Such type of system is used to identifying

the source, author, creator, owner, and distributor or authorized consumer of a

multimedia data.

1.3.3 Blind watermarking system

A watermarking technique is said to be blind, if to extract the watermark from

watermarked data it does not need original image. The blind watermarking system

is also known as oblivious. Blind watermarking system is more popular because

it decreases the overhead of cost and memory for storing original data.

1.3.4 Non-blind watermarking system

The watermarking techniques in which to extract the watermark, it requires the

original data is known as non-blind watermarking system. It is more robust than

blind watermarking system.

1.3.5 Robust watermarking system

A watermarking system is said to be robust, if any modification on the water-

marked data results in no change into watermark value. That is extracted water-

mark information from the tampered watermarked data would be same as original

watermark information. A robust watermarking system resist against wide range

of intentional and unintentional attacks such as, image enhancement, filtering,

noise addition, JPEG compression and geometrical transformations, collusion and

forgery attacks.

Robust watermarking systems have been proposed to be implemented in num-

ber of application. Such as copyright protection, finger printing and access control.

5

1.4 Properties of Digital Watermarking

Copyright protection is one of the main applications of robust watermarking sys-

tem. In copyright protection application the idea is to embed information about

the copyright owner into the multimedia data to prevent parties from claiming to

be the rightful owners of the data. The robust watermark embedded into the con-

tent is detectable despite common image processing manipulations. finger printing

is used to trace authorized users who violate the license agreement and distribute

the copyrighted material illegally. Thus, the information embedded in the content

is usually about the customer such as customer’s identification number.

1.3.6 Fragile watermarking system

In fragile watermarking system embedded watermark in host data can be eas-

ily destroyed. This property is useful to identify whether a multimedia data is

modified/ manipulated or not? By embedding then fragile watermark into mul-

timedia data, the authenticity of multimedia data can be achieved. Any small

manipulation on the watermarked data will lead to distortion in corresponding

embedded fragile watermark. At the end side by comparing the extracted water-

mark with original watermark, it can be easily identified whether the multimedia

data is manipulated or not. The different applications where fragile watermark-

ing can be used are document authentication, evidence authentication, complete

authentication etc.


An effective digital watermarking algorithm must have number of properties. This

section describes the number properties of digital watermarking algorithm.

1.4.1 Imperceptibility

The basic requirement of digital watermarking is to have the watermarked im-

age should look alike as the original image. This confirms there is not much

degradation on the original image. This property is known as imperceptibility or

transparency of the watermarking system [11]. The embedded watermark should

not be visible to human eye. To calculate the imperceptibility, generally Peak

Signal to Noise Ratio (PSNR) is used [11].

6


1.4.2 Robustness

The capability of survival of watermark against both legitimate and illegitimate

attacks is referred as robustness. All watermarking system needs to resists against

any legitimate and illegitimate attacks, except fragile watermarking system. For

manipulation recognition in original data the watermark has to be fragile to detect

altered media. Robustness depends on watermarks information capacity, visibility

and strength. Generally a good watermarking algorithm should be robust against

filter processing, noise addition, geometrical transformations such as rotation, scal-

ing, translation and lossy compression such as JPEG compression [12].

1.4.3 Security

The watermarking system should be secured i.e. hacker should not be in position

to extract the watermark without having the knowledge of embedding algorithm.

Watermarking system must be capable of stand firm against different attacks

[2]. Attacks try to remove, modify or embed (unwanted information) into the

watermark. Attacks are mainly classified in two different types i.e. passive attack

and active attack. Passive attack only detects the watermark information, while

active attack tries to modify the watermark information.

1.4.4 Complexity

The time and effort needed to embed and retrieve the watermark information

is known as complexity of the watermarking system. The complex algorithm in

watermarking system requires more software and hardware resources to implement

it, which results in increasing the computation cost. To reduce the computational

cost of watermarking system, it should be less complex. Such as in telemedicine

domain, to cut the cost of bandwidth consumption during the transmission of

medical data less complex watermarking algorithms are implemented.

1.4.5 Capacity

Capacity of the watermarking system describes embedding of maximum amount

of watermark information i.e. embedding the multiple watermarks in single data.

The higher capacity of embedding information in a data can be obtained by com-

promising either imperceptibility or robustness of algorithm [13].

7

1.5 Watermarking Techniques

1.4.6 Invertibility

This property of digital watermarking system describes the possibility of generat-

ing original data during the extraction process of watermark.

1.4.7 Verification

This property defines the procedure of verification i.e. private key verification and

public key verification, depending on its respective algorithm.


There are different kinds of watermarking techniques are in place, which are differ-

entiated on the basis of types of document, types of domain, etc [14]. The various

types of watermarking according to different categories are shown in Figure 1.2.

Watermarking techniques are broadly divided into four types:

1. According to working domain

2. According to types of document

3. According to human perception

4. According to application

These four categories are further classified as below

1. According to types of document

• Text watermarking

• Image watermarking

• Audio watermarking

• Video watermarking

2. According to human perception

• Visible watermarking

• Invisible watermarking

8


Figure 1.2: Types of Watermarking Techniques

3. According to Application

Source based watermarking: This approach is used for the ownership au-

thentication where unique watermark is embedded into all copies of data.

Destination based watermarking: This approach is used in the application

where the tracing of buyer is done for the purpose of illegal reselling. Here

for each distributed copy a unique watermark is used.

4. According to working domain

• Spatial domain

• Frequency domain

Watermark can be applied in spatial domain or it can be applied in frequency

domain.

1.5.1 Spatial domain watermarking

Spatial domain watermarking method hides the watermark directly within the host

data [15, 16]. This approach is easy and simple to implement. The advantage of

this approach is the spatial localization of the embedded data can be achieved

automatically even after the watermarked content goes under some attacks. An-

other advantage of spatial domain watermarking is that, it allows the control on

maximum difference between the original image and watermarked image due to

which design of near-lossless system can be possible [13]. Spatial domain water-

marking is applied in number application. There are various ways of applying

9


spatial domain watermarking.

(a) Additive Watermarking

Additive watermarking is most straightforward method for embedding the water-

mark in spatial domain. It adds pseudo random noise pattern to the pixel of host

data. To ensure that embedded watermark should be detected, the noise to add

in host data is generated by a key. The same key is used at the time of extraction

process.

(b) Least Significant Bit Modification

This method is very common for embedding the watermark in the host data. It

relies on the way of manipulating the LSBs of host data, in a manner which is not

detectable by human eye. The basic idea for this method is to replace LSBs of

host data by same size of binary watermark.

1.5.2 Frequency domain watermarking

However, the spatial-domain watermark insertion is simple and easy to implement,

but it is fragile versus various attacks and noise. To get the better robustness as

well as imperceptibility, watermarking is done in frequency domain. Frequency do-

main is also known as multiplicative watermarking. There are several watermark-

ing techniques in different frequency domain such as Discrete Fourier Transform

(DFT), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT),

Discrete Curvelet Transform, and Discrete Counterlet Transformation [17]. This

section covers the details of DWT domain.

Discrete Wavelet Transform (DWT)

All transform domain watermarking algorithms generally follows three steps i.e.

(i) Data transform (ii) watermark embedding and (iii) Watermark recovery. Trans-

formation of host data can be applied either on whole data [18], or in block by

block manner [19]. Wavelets are mathematical function that cuts the data into

different frequency components, and according to the resolution matched to its

scale wavelet function study each component. The advantage of wavelet trans-

form over traditional Fourier methods is that it analyses the signal which contains

discontinuities and sharp spikes. Other advantage of wavelet transform is, it cap-

tures both frequency and location information (location in time). The basic idea

for 1D DWT is it decomposes the signal (host data) into high frequency part and

low frequency part. The edge components of the signal are largely contained to

10


the high frequency part. The low frequency part is again split into two parts low

and high frequency part. This process can be continued till an arbitrary number,

which is usually determined by the application at hand. Furthermore, the original

signal can be reconstructed by inverse DWT (IDWT) process. In case of 2D-DWT

we get four subbands from one level, that are Low-Low level (LL), High-High Level

(HH, Low-High level (LH) and High-Low level (HL). The LL subband contains the

low level details of the image. In the next level, the 2D-DWT of the LL subband

is obtained and this is repeated in each succeeding level.The filter bank structure

used in wavelet decomposition of an image is shown in Figure 1.3. Where h[n] is

high pass filter, g[n] is low pass filter and W is wavelet function.

Figure 1.3: The filter bank structure used in wavelet decomposition of an image

H[n] and G[n]are defined as below:

H[n] =∑k

hke−jkn (1.3)

11

1.6 Application of Watermarking

G[n] =∑k

gke−jkn (1.4)

There are number of basic function that can be used to perform wavelet transform

on given signal. Such as, lazy, haar, daubechies wavelets (db1, db2, db3, etc.),

meyer, etc.

1.6 Application of Watermarking

Increasing research on watermarking from the past decades has been largely mo-

tivated by its applications in copyright management and protection. The digital

watermarking technique is highly suitable for medical, military, and archival based

applications.

• Broadcast monitoring is the well known application of watermarking, which

helps advertising agencies to track the specific video broadcast by a TV

Channel or station. Embedding the watermarked video to the host video

will provide you easier way to track and monitor the broadcast.

• Owner Identification is also a well known application of watermarking, which

helps in identifying the owner of video or image. Such as copyright authori-

ties, where instead of providing copyright notice with every image or video

the watermark could be directly embedded in to the image or video itself.

• Another well know application of watermarking is copy control which helps

preventing the illegal copy of songs or images of movies etc. Where by

embedding watermark in songs or images of movie would instruct a water-

marking compatible DVD or CD writer to not write the song or movie as it

is an illegal copy.

• With the help of watermarking Transaction Tracking can be achieved by

recording the transaction details in the history of a copy in digital work.

For example issuing each recipient a legal copy of movie by embedding the

watermark (different watermark for different recipient) will help in tracking

the source of leak in case of movie leaked to the internet.

• Medical image watermarking is one of the important applications of wa-

termarking. Medical image authentication systems which can not only au-

thenticate medical images but would also be able to secretly communicate

auxiliary information can be achieved by watermarking technique. Only the

12

1.7 Thesis Outline

authorized people of the hospital would thus be able to modify the content

of medical image.

1.7 Thesis Outline

The outline of report is described below:

Chapter 2 provides brief introduction to the medical image watermarking, where

it explains the requirements of medical image watermarking. The study of differ-

ent segmentation algorithm in place, to separate the ROI from medical image and

the available algorithm for medical image watermarking are discussed in chapter 3.

Chapter 4 discuss about the proposed system for medical image watermarking

preserving ROI. The proposed system has been applied successfully against all

existing medical imaging. Chapter 5 shows the experimental results achieved by

using the proposed system. Chapter 6 provides insight on conclusion and the

future work.

13

Chapter 2

Medical Image Watermarking

Introduction

Speedy development of internet in every field leads to availability of digital data

to the public. Internet has been spread in many applications like telemedicine,

online-banking, teleshopping etc. One of this application telemedicine is crucial

one, where Internet is used to transfer or receive medical data by healthcare pro-

fessional. Due to advancement in information and communication technologies, a

new context of easier access, manipulation, and distribution of this digital data

have been established [20]. The medical images can be readily shared via com-

puter networks and easily used, processed, and transmitted by using great spread

network [21, 22].

In the last decades, uses of advanced electronic and digital equipments in

health care services are increased, where traditional diagnosis system has been re-

placed by e-diagnosis system. In fact, in most of the hospitals physicians diagnose

their patients by relying on the provided electronic and digital data (such as Ul-

trasonic, Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and

X-ray images). This results in generation of large number of electro digital data

(i.e. medical images) continuously at various health care centers and hospitals

around the world. The typical e-diagnosis model is shown in Figure 2.1, where

medical image can be sent by patient through the internet to physician. One

physician can transfer the medical image to anther physician for second opinion.

The medical images are stored in patient historical database for future diagnosis.

In number of medical applications, special safety and confidentiality is required

for medical images, because critical judgment is done on medical images, which

14

leads to the proper treatment. Therefore, it must not be changed in an illegitimate

way; otherwise, an undesirable outcome may results due to loss of decisive infor-

mation. Therefore, there is a need to provide a strict security in medical images to

ensure only occurrence of legitimate changes. Now-a-days exchange of medical im-

ages between hospitals located in different geographical location is very common.

Moreover, as this exchange of medical reference data done via unsecured open

networks leads to the condition of changes to occur in medical images and creates

a threat which results in undesirable outcome. Considering this fact, demand of

security is getting higher due to easy reproduction of digitally created medical

images. For copyright protection and authentication of these medical images, dig-

Figure 2.1: A typical e-diagnosis Model

ital watermarking is an emerging technique, which includes the embedding and

extraction process. Embedding process hides some secrete information in to med-

ical images. This secret information is extracted during the extraction process. If

failure occurs in extraction process the physician would come to know that there

has been some kind of tampering with that image, and he would take precaution

of not making diagnosis based on that image. However, if the extraction process

extracts the correct watermark, which generally consumes a few seconds, physician

can continue with diagnosis.

15

2.1 Principle of Medical Image Watermarking

2.1 Principle of Medical Image Watermarking

The typical block diagram for medical image watermarking is given in Figure 2.2.

Encoder E embeds the watermark W in medical image to provide security and

authentication. Decoder D extracts the watermark from watermarked image. By

comparing the extracted watermark with original watermark, one can affirm the

tampering of medical image.

Figure 2.2: Block diagram of Medical Image watermarking

To ensure the reliability and quality of the watermarked image, the performance

of watermarking is calculated, which measured in terms of perceptibility. There

are two method of calculating the performance measure.

• Mean Square Error (MSE):

It is simplest function to measure the perceptual distance between water-

marked and original image. MSE can be defined as:

MSE =1

n

n∑i

(I ′ − I)2

(2.1)

Where, I is original image and I is watermarked image.

• Peak Signal to Noise Ratio (PSNR):

It is used to measure the similarity between images before and after water-

marking.

PSNR = 10 log10

maxI2

MSE(2.2)

Where, max I is the peak value of original image.

16

2.2 Requirements of Medical Image Watermarking

2.2 Requirements of Medical Image Watermark-

ing

2.2.1 Imperceptibility

Imperceptibility is one of the strict requirements of the medical image watermark-

ing. Imperceptibility means the embedded watermark should not be visible by

human eye. It is often not allowed to alter the medical image contents even after

embedding the watermark in some application [23, 20]. The imperceptibility in

medical image watermarking can be achieved by two methods. In first method

imperceptibility is fulfilled by selecting the Region of Non Interest watermarking

[24], in which the watermark is embedded in RONI area of medical image. In

this method the Region of Interest (ROI) area of medical image will be distortion

free. Imperceptibility can be achieved by reversible watermarking method which

recovers the original medical image by undoing the watermark embedding process

at the receiving side [25].

2.2.2 Capacity

In medical image watermarking, all the information that are required by the physi-

cian such as identification of patient, doctor identification, treatment, etc are em-

bedded in medical image. Therefore, capacity for embedding the payload must be

high.

2.2.3 Authenticity

The entitled users (patient, doctor) should have the access to the medical data.

This can be achieved by embedding the doctors identification and patient identi-

fication in medical images.

2.2.4 Reversibility

At the receiving side the reverse of embedding process should be possible to get

the original medical image and embedded watermark. This property is known as

reversibility of medical image watermarking.

17

2.2 Requirements of Medical Image Watermarking

2.2.5 Complexity

According to the e-diagnosis the medical images are transferred from some remote

location to the other side through internet. In such cases speed becomes an im-

portant matter, thus the algorithm should be less complex to reduce the execution

time.

2.2.6 Intactness of ROI

Medical images hold decisive property and are very crucial and important part

of medical information. Such part of the medical image is called as Region of

Interest (ROI). The ROI is helpful in providing further diagnosis by the physician.

A small bit of distortion in ROI may lead to undesirable treatment for patient. For

securing medical images through watermarking ROI should be preserved and the

watermarks can be applied on the remaining part of the image called as Region of

Non Interest (RONI). Therefore, application of watermarking in medical images

can be considered as two-step process which includes:

1. Extracting ROI form the medical images

2. Applying watermarking on RONI

18

Chapter 3

Literature Survey

Different algorithms are available for segmentation of ROI on the different types of

medical images. Additionally, there are different algorithms available for applying

watermarking.

3.1 Region of Interest (ROI) Segmentation

Segmentation plays an important role in medical image processing [26, 27]. In

medical image analysis segmentation is the first step to be followed, to avoid dis-

tortion of ROI [26, 28]. Image segmentation deals with the process of partitioning

an image into different regions by grouping together neighborhood pixels based on

some predefined similarity criterion [29]. This similarity criterion can be defined

by specific properties of pixels in the image. Segmentation in medical imaging

is used for the extracting the features, image display and for the measurement of

image. The goal of segmentation is to divide entire medical image in to sub regions

i.e. (white and gray matter). In addition, this helps in classifying image pixels

in to anatomical regions (such as bones, muscles and blood vessels). Defining

the borders of ROI in medical image can simplify the procedure of segmentation.

In addition, the step of defining borders of ROI is a crucial one, which helps

in determining the result of the application as entire analysis fully relies on the

output from segmentation step. There are different approaches (for segmenting

the image) defined for the different imaging technologies such as CT, MRI, US,

colonoscopy etc. Segmentation is semi-automatic procedure and we need to define

a seed point in an image. Therefore, the algorithm, which gives perfect result for

one application, might not even work for another. Figure 3.1 shows the ROI part

of medical image, where physician performs the diagnosis.

We have various existing medical imaging like Computed Tomography (CT),

19


Figure 3.1: Medical image indicating ROI

Magnetic Resonance Imaging (MRI), Ultrasound (US), and Positron Emission

Tomography (PET) etc. Here, two most common imaging i.e. MRI and CT scan

are discussed in detail with their proposed algorithms.

3.1.1 Magnetic Resonance Imaging (MRI)

Magnetic Resonance Imaging (MRI) provides a wealth of information, which is

useful for medical examination. In many applications where MRI is used, segmen-

tation of image into different intensity classes are needed, which is regarded as the

best available representation for biological tissues [30, 31]. Segmentation plays

very important role in MRI process for deciding the spatial location, selecting the

operation path, shape, and size of the focus etc. In segmenting MRI images, main

requirement is to care about three problems: noise, partial volume effects (where

more than one tissue is inside a pixel volume), and intensity in-homogeneity [32].

Due to irregularities of the scanner magnetic fields-static (BO), radio frequency

(B1), and gradient fields, intensity in-homogeneities are caused. These irregu-

larities results in producing spatial changes in static tissues of MRI data. When

multiple tissues contribute to a single voxel, by making the distinction between tis-

sues along boundaries more difficult leads to the problem of partial volume effects.

Adding noise in MRI images can encourage disconnection between segmentation

regions. Therefore, for doing segmentation of MRI data on these three difficulties

need to focus. There are four different approaches for doing image segmentation:

20


thresholding, clustering, edge detection, and region extraction. This section covers

various available MRI segmentation algorithms based on following approaches:

• Thresholding approach

• Clustering approach

• Edge detection approach

1. Thresholding: Thresholding is one of the easiest and most frequently used

techniques to segment MRI data by separating the foreground from back-

ground of image [33, 34]. Thresholding approach can further be classified as

global thresholding and local (adaptive) thresholding. In global thresholding

method, image segmentation is done by providing single threshold value in

the whole image whereas, in local thresholding, threshold value is assigned to

each pixel of image by using local information around the pixel, and then to

determine whether the particular pixel belongs to foreground or background

these threshold values are used. Due to simplicity and easy implementation

of global thresholding, this method is more popular. P-tile method is one of

the earliest thresholding methods based on the gray level histogram [33, 35].

Here P refers to the word percentile. This algorithm stands on the statement

Objects in the image are brighter than the background, which occupy a fixed

percentage of the picture area. In this algorithm, threshold is defined as the

gray level that mostly corresponds to mapping at least P% of the gray level

into the object. The experimental results of this method specify that it is

suitable for all size of objects, and it provides good anti-noise capabilities.

However, this method is not applicable in application where object area ratio

is unknown or varies from image to image.

2. Clustering: The goal of clustering approach is to group similar objects and

separates the dissimilar objects. That is depending on some perceived simi-

larities this grouping of pixels is done. These clusters then lead in providing

natural partitions of pixels that corresponds to the different regions in an

image. Conventional clustering algorithms require a prior knowledge regard-

ing the number of clusters, clustering criteria, and nature of data, etc. There

are many algorithms defined for the clustering, such as K-means clustering,

Fuzzy c-means (FCM) clustering, possibilistic c-means, possibilistic-fuzzy

clustering, intuitive fuzzy c-means (IFCM), and so on. The objective of clus-

tering, for a given set of unlabeled N samples or data i.e. X= x1, x2, ...., xn

21


is to assign a class label among C labels to each of N samples. This num-

ber of labels C, considered as the number of regions or number of groups.

One of the most widely used clustering algorithms is Fuzzy c- means (FCM)

[36, 37]. In the FCM algorithm, it assigns labels to the data, which are in-

versely related to relative distance of to the point prototypes that are cluster

centers in FCM model. In FCM, proximity of each data or samples, xk, to

the center of cluster, vi, is defined as membership or label (uki) of data xk

to the ith cluster of X with following conditions:

0 ≤ uki ≤ 1andc∑

i=1

uki = 1,∀k (3.1)

U = [uki]NxC andV = v1, v2, ....vi (3.2)

FCM algorithm is acknowledged as one of the best clustering algorithm as it

resolves various problems, [36, 38], although it still suffers from undesirable

solutions with outliers data [36, 39]. As in FCM algorithm, it requires to

provide the exact number of clusters in advance as a prior knowledge. The

exact estimation of number of clusters in MRI images, (used in particular

for diseased cases), may not be possible to have in advance. To overcome

this problem, Krishnapuram and Keller proposed a clustering model named

possibilistic c-means (PCM) [36, 38]. In the PCM model the condition of

FCM model,

C∑i=1

uki = 1 ∀k, is relaxed by introducing the new condition as (3.3)

C∑i=1

uki = C ∀k (3.4)

By providing new condition, PCM model improves its performance over the

FCM algorithm, as PCM overcomes the drawback of FCM. PCM is ISO-

DATA based algorithm, which makes use of user defined criteria for merging

and splitting clusters to discover the number of natural clusters in the data.

However, it is very difficult to define these splitting and merging criteria that

can be applied on various MR data based on prior assumptions of intensity

distribution. Hence, PCM model is very sensitive to initialization and need

22


of additional parameters [36, 40]. Afterward a possibilistic-fuzzy clustering

(PFCM) model is proposed. In PFCM algorithm both FCM and PCM model

combined by introducing two new parameters a and b, where a and b are

the weighting factors of FCM and PCM, respectively, to resolve the outlier

problems of FCM and sensitivity problem. Again, in PFCM algorithm as it

requires selecting additional parameters and extra computation complexity,

a new model intuitive fuzzy c-means (IFCM) model is proposed by Dong-

Chul Park for MRI image segmentation. The basic operation of IFCM model

is same as in FCM except the membership assignment condition. To deal

with the problems of membership assignment to noise data IFCM algorithm

has been developed. In IFCM, a new measurement called intuition level is

introduced by using membership values of FCM and PFCM, uki so that the

intuition levels may alleviate the effect of noise data.

3. Edge Detection: For doing the segmentation by using edge detection ap-

proach, first step is to extracts the features by obtaining the information

from images. Edge detection is a fundamental tool used in most image

processing application. It is the process of detecting boundaries between

objects and the background in the image, at which the image brightness

changes sharply. There are many algorithms to perform edge detection, and

all of them classified into two categories Gradient and Laplacian.

Edge detection based on Gradient method initially calculates first derivative

of image, and then find its corresponding local maxima and minima values

to detect the edges. While, in the Laplacian method after obtaining the

second derivative of the image it looks for zero crossing. There are various

operators defined i.e. Roberts [41], Prewitt [42], Sobel [43], Canny [44] edge

operators to perform Gradient method. These operators include a small ker-

nel rolled up together with the image, which helps in estimating first order

directional derivative of the image brightness distribution. This kernel finds

edge strength in the direction, which are orthogonal to each other, usually

vertically and horizontally. The total value of the edge strength is then

obtained by the combination of both the components. Here by creating a

matrix centered on each pixel it calculates the edge value. Moreover, if the

calculated value is larger than provided threshold, then that pixel is classi-

fied as an edge.

23


With reference to the earlier work of the Marr and Hilderth [42], John F.

Canny [44] has developed an edge detection operator named as Canny edge

detection operator in 1986. This operator helps in detecting wide range

of edged in images by using a multi-stage algorithm. He provided gradient-

based-finding algorithm called as optimal edge detector, which becomes most

popular and commonly used edge detectors to have the segmented image.

Canny edge detector used a method called Hysteresis, which is proposed to

tracing the unsuppressed pixels. Here it uses two threshold values i.e. high

and low. After finding the gradient values, algorithm compares these values

with the provided two threshold values. The pixel is set to zero if gradient

value is below the low threshold value and if it is above the high threshold

value then pixel is set as an edge. In case if, the gradient value is in between

the two threshold values by default that pixel is set to zero (regarded as non-

edge) although there is a path from that pixel to the pixel having gradient

value above the high threshold value.

3.1.2 Computed Tomography (CT)

Computed Tomography (CT) scanning sometimes called Computed Axial Tomog-

raphy (CAT) scanning [29], is a noninvasive medical test that helps physicians

diagnose and treat medical conditions. CT scanning combines special x-ray equip-

ment with sophisticated computers to produce multiple images or pictures of the

inside of the body. These cross-sectional images of the area being studied can then

be examined on a computer monitor, printed or transferred to a CD. CT scans

of internal organs, bones, soft tissue and blood vessels provide greater clarity and

reveal more details than regular x-ray exams. Using specialized equipment and

expertise to create and interpret CT scans of the body, radiologists can more eas-

ily diagnose problems such as cancers, cardiovascular disease, infectious disease,

appendicitis, trauma, and musculoskeletal disorders. Hence, the CT scan appli-

cation is been widely used in medical domain. There are different segmentation

methods proposed considering CT scan of different body organs (such as lung,

liver, kidney, etc.) This section covers the different segmentation algorithm for

CT scan images for protecting the distortion of diagnosis value.

The 2-D and 3-D segmentation of organs in medical application of image pro-

cessing are classified into model based and nonmodel based approaches. Nonmodel

based approaches depends on local information such as, texture, intensity, spatial

24


correlation of 2-D organ image in consecutive slices, and the location of the organ

in the abdominal area with respect to neighboring structures, e.g., spine and ribs

[45]. Various segmentation algorithms are developed using nonmodel-based ap-

proach. This section first covers the different segmentation algorithm, which uses

nonmodel-based approach. Susomboon et al. [46] presented texture features to

perform region classication for extracting livers soft tissue. Seo et al. [47] employed

a multimodal threshold method based on piecewise linear interpolation that used

spine location as a reference point. Forouzan et al. [48] introduced a multilayer

threshold technique, in which by statistical analysis of the liver intensity it cal-

culates the threshold value. Both these methods use the local information of the

livers relative position to the spine and ribs. Nonmodel-based methods for organ

segmentation leads to inaccuracies due to variation in imaging condition, because

of occurrence of tumor inside the organ and noise. Dependencies on prior infor-

mation such as texture and image values could cause inaccuracies in segmentation

process as feature could change from one patient to another. Moreover, most of

these methods are parameter dependent and hence for the best performance it

often needs to adjust the parameters from one CT volume to other. In recent

years, model-based image segmentation algorithms developed for various medical

applications. These methods aim to recover an organ based on statistical informa-

tion. State-of-the-art algorithms on model-based segmentation are based on active

shape and appearance models [45]. Model-based techniques provide more accu-

rate and robust algorithm for segmenting the CT scan image. These techniques

also deal with the missing image features via interpolation. The performance of

these methods depends on the number and type of training data. Moreover, if

the shape to be segmented lies too far from the model space, that might not be

detected by many those better methods which does not implemented by statistical

model-based approach.

Pan and Dawant [49] reported a geometrical-level set method for automatic

segmentation of the liver in abdominal CT scans without relying on the prior

knowledge of shape and size. Even if this method depends on a model-based

technique, that outperforms threshold-based techniques, but it did not use prior

knowledge of the liver shape. Lin et al. [51] presented the algorithm to perform

segmentation of kidney, based on an adaptive region growing and an elliptical

kidney region positioning that used spines as landmark. H. Badakhshannoory

and P. Saeedi [51] incorporated a method for liver segmentation. Based on liver

boundary edges to identify liver regions, nonrigid registration and a multilayer

25


segmentation technique are combined in this approach. This method is does not

affected by the diversity of existing liver shapes, as it does not rely on any shape

model. Samuel et al. [52] has proposed the use of Ball-Algorithm for the seg-

mentation of lungs. In this algorithm at the first stage, it applies the grey level

thresholding to the CT images to segment the thorax from background and then

the lungs from the thorax. Then in the next step to avoid loss of juxtapleural

nodules, this method performs the rolling ball algorithm. Julian Ker [53] has pre-

sented the method of doing segmentation of lungs, which is named as TRACE

method. Due to the possible presence of various disease processes, and the change

of the anatomy with vertical position results in variation of size, shape, texture

of lungs CT image of different patients. Therefore, the boundary between lung

and surrounding tissues can vary from a smooth-edged, sharp-intensity transition

to irregularly jagged edges with a less distinct intensity transition. The TRACE

algorithm implemented with new perception of a non-approximating technique for

edge detection. Shiying et al. [54] have introduced a fully automatic method for

identifying lungs in 3D pulmonary X-Ray CT images. The method follows three

main steps:

• lung region is extracted from CT-Scan image by applying graylevel thresh-

olding,

• by using a dynamic programming it identifies the anterior and posterior

junction, to separate left and right lungs and

• to smooth the irregularities of boundary along the mediastinum nodule, it

implements sequence of morphological operations

Ayman El-Baz et al. [55] have employed a fully automatic Computer-Assisted

Diagnosis (CAD) system for lung cancer screening using chest spiral CT scans.

This paper presents a system for detection of abnormalities, identification or clas-

sification of these abnormalities with respect to specific diagnosis, and provides

the visualization of the results over computer networks. The process of detec-

tion of abnormalities, identification of these abnormalities can achieve by image

analysis system for 3-D reconstruction of the lungs. Riccardo Boscolo et al. [56]

proposed method that uses the novel segmentation technique that combines a

knowledge based segmentation system with a sophisticated active contour model.

This method performs robust segmentation of various anatomic structures. In this

approach the user, need to provide initial contour placement, and the required

parameter optimization automatically determined by the high-level process. Bin-

sheng et al. [57] reported the algorithm, which used the method of selecting the

26

3.2 Medical Image Watermarking

threshold value by analyzing the histogram. This algorithm initially separates the

lung parenchyma from the other anatomical structures from the CT images by

using threshold value. By this algorithm structure in CT scan image with higher

densities having some higher density nodules, can grouped into soft tissues and

bones, leading to an incomplete extraction of lung mask. For having complete

hollow free lung mask, morphological closing is applied in this approach. Hossein

B. et al. [45] has introduced the model-based segmentation algorithm. In this

approach instead of using model information to direct the segmentation algorithm

for segmenting an organ of CT scan images, it uses this information to choose a

segment with highest fidelity to the organ.

After completing with the segmentation of ROI, needs to proceed with medical

image watermarking technique to provide security, authentication and privacy of

this medical data. The next section of this paper provides the survey of different

available medical image watermarking approaches.


There has been fair amount of work done in the area of medical image processing.

Numbers of medical image watermarking schemes are reported in this literature

survey, to address the issues of medical information security, and authentication.

Wakatani [58] presented a medical image watermarking, in order not to com-

promise with the diagnosis value, it avoids embedding watermark in the ROI. In

this algorithm watermark to be embed is firstly compressed by progressive coding

algorithm such as Embedded Zero Tree Wavelet (EZW). Embedding process is

done by applying Discrete Wavelet Transform (DWT), for transforming the orig-

inal image using Haar basis. Extraction of watermark is reverse of embedding

process. The major drawback of this algorithm is ease of introducing copy attack

on the non-watermarked area. Yusuk Lim et al. [59] reported a web-based image

authentication system, they used the CT scan images. This technique is mainly

based on the principal of verifying the integrity and authenticity of medical im-

ages. In this approach, the watermark is preprocessed by using 7 most significant

bit-planes except least significant bit (LSB) plane of cover medical image, as a

input to the hash function. This hash function generates binary value of 0 or 1

using secrete key, which is then embedded in LSB bit of cover image to get wa-

termarked image.

27


Rodriquez et al. [60] proposed a method in which it searches a suitable pixel

to embed information using the spiral scan that, starts from the centroid of cover

image. Then by obtaining the block with its center at the position of selected

pixel, it checks the value of bit to embed. If bit value is 1, then the embedding

information is obtained by changing the luminance value of the central pixel by

adding the gray-scale level mean of the block with luminance of the block. In

addition, if bit value is 0, then luminance value of the central pixel is changed

by subtracting the luminance value of block from the gray-scale level mean of

the block. While in extraction process, the position of marked pixel is obtained

by spiral scan starting from centroid of the cover image. By checking the lu-

minance value of the central pixel with the gray-scale level mean of the block,

embedded bit is identified. Giakoumaki et al. [20] presented a multiple water-

marking method using wavelet-based scheme. The method provides solution to

the number of medical data management and distribution issues, such as data

confidentiality, archiving and retrieval, and record integrity. In this approach up

to 4 level DWT is performed on medical image. The algorithm embeds multiple

watermarks in different level. A robust watermark containing doctors identifica-

tion code is embedded in 4th level as here capacity is not the matter, only required

is the robustness. In third level decomposition, the index watermark (e.g ICD-10

or ACR diagnostic codes) is embedded. The method embeds caption watermark

holding patients personal information in second decomposition level. Moreover, a

fragile watermark is embedded in forth-level decomposition. Extraction process

is reverse of embedding process. Experimentation is done on ultrasounds medical

images.

Hemin Golpira et al. [61] reported reversible blind watermarking. In this ap-

proach during embedding process, firstly by applying Integer Wavelet Transform

(IDWT) image is decomposed into four subbands. By selecting two points, called

thresholds, according to the capacity required for the watermark data, water-

mark is embedded. To get watermarked image Inverse Integer Wavelet Transform

(IIDWT) is applied. In the extraction process, all of these stages are performed

in reverse order to extract watermark as well as host image.

Nisar Ahmed Memonet et al. [62] presented fragile and robust watermarking

technique for medical images. The method embeds two different watermarks, the

robust watermark and fragile watermark in the medical images. The embedding

28


process is start with separation of ROI and RONI from medical images. The

robust watermark containing the electronic patient record (EPR), Doctors identi-

fication code (DIC) and 1st bit-plane of ROI by extracting the LSBs is encrypted

by using pseudo random sequence generated by user defined key. Then this resul-

tant watermark is embedded in high frequency coefficient of IWT in RONI part

of medical data. The proposed method generates fragile watermark by creating

the binary image in tiled fashion and then this fragile watermark is cropped off

by the same size as the ROI. The algorithm embeds this fragile watermark into

spatial domain of ROI part of medical image. The extraction process is reverse of

embedding process.

29

Chapter 4

Proposed System for Medical

Image Watermarking preserving

ROI

Our approach focuses on embedding watermark in RONI region of medical image

by preserving ROI. This approach helps in isolating ROI region i.e. not to distort

the critical area of medical image, which will be referred by physician for the

diagnosis. The system diagram for this approach is shown in Figure 4.1. The

system process carried away in three stages:

1. Watermark embedding process

2. Watermark extraction process

3. Watermark authentication process

In first phase of system separating the ROI from the original medical image

provides RONI region for embedding watermark. This step isolates ROI from em-

bedding process. In this phase multiple watermarks are embedded into the RONI

area of medical image. Embedding multiple watermarks ensure high security of

medical image as it carries high payload and it will be more complex to break the

system. Here fragile watermarking system is used to get the benefit of identifying

whether a medical image is tampered or not?

After the completion of embedding process the separated ROI is combined

with the produced watermarked medical image. The resultant watermarked med-

ical image is then sent to the receiver.

30

4.1 Separating ROI from medical image

Figure 4.1: Medical Image Watermarking Approach Preserving ROI

In watermark extraction phase, first step is separating the ROI from the wa-

termarked medical image. The remaining watermark extraction process is exact

reverse of embedding process, where the embedded watermark will be extracted

from the watermarked medical image. The watermark authentication is achieved

by comparing the extracted watermark with the original watermark. This process

helps in identifying if any tampering or manipulation to the watermarked medical

image over the public network.


As discussed earlier for separating ROI Segmentation method is used. However

segmentation is semi-automatic procedure and it needs to define a seed point in

an image. Therefore, the algorithm, which gives perfect result for one type of

application, may not even work for another.

In proposed system for separating ROI the Graphical User Interface (GUI) is

implemented, so that it will work for all kinds for medical image (such as CT

scan, MRI, X-Ray, Ultrasound, etc.). The interface for the implemented GUI

based approach is shown in Figure 4.2. In this method user has an option to

select the part of medical image (square in size) which has critical information

and used for the reference of physician. This GUI based system returns the Xmin,

31


Figure 4.2: Interface for GUI based approach

Xmax, Ymin, Ymax pixel values of selected ROI region and image of selected

ROI. This resulted ROI image can be saved, so that it can be combined with the

resultant watermarked image. The dashed square in Figure 4.2 is the user selected

ROI of medical image, the region that can be selected by mouse click function.

The respective pixel values (Xmin, Xmax, Ymin, Ymax) are shown at top of the

window panel.

Steps in ROI separation approach

• Mouse click function: For selecting the ROI, mouse clicking function is used.

• Done button: To get the output after selection process, done button is im-

plemented.

• Storing handles: For safe storing the pixels values of selected ROI (Xmin,

Xmax, Ymin and Ymax) and image of selected ROI, the storing handles are

use.

• Start button: It is implemented to clear the stored handles to start again

the process of selecting ROI.

• Zooming option: It is provided for zooming the image, so that the image

will be clear to select the ROI.

32

4.2 Medical Image Watermarking System

4.2 Medical Image Watermarking System

For the implementation of Medical Image Watermarking, we referred the algo-

rithm proposed by Giakoumaki et al. [20]. The algorithm provides solution to

the number of medical data management and distribution issues, such as data

confidentiality, archiving and retrieval, and record integrity. The medical water-

marking system embeds the multiple watermarks. The watermarks used to embed

are in the text form. In this approach medical image is decompose with 4-level

lifting based DWT transform. The lifting based DWT is a better method to ob-

tain the wavelet transform. For the development of second generation wavelet the

lifting based DWT approach is proposed. Advantage of second generation wavelet

over first generation wavelet is that, it does not use the translation and dilation

of the same wavelet prototype in different levels. Using the Euclidean algorithm

any classical wavelet filter bank can be decomposed into lifting steps. The lifting

based DWT consists of three stages i.e. split, predict and update. In split stage

the input signal x[n] get divided into two subsets i.e. even set s[n] and odd set o[n].

This process is known as lazy wavelet transform. The predict step use the linear

combination of elements in one subset to guess the values of the other subset with

assumption that the subsets produced in the split stage are correlated with each

other. The predicted values would be close to the original values if the correlation

between both the subset is high. Generally the linear combination of the even

subset elements are used to predict odd subset values. The predict step is defined

as:

d[n] = o[n]−∑k

p[k]s[n− k] (4.1)

Where d[n] is the difference between the actual values and the predicted values,

P[k] is prediction coefficient. Although there are chances of loss of properties of

signal such as mean value in the predict step.

s1[n] = s[n]−∑k

u[k]d[n− k] (4.2)

The predict step causes the loss of some basic properties of the signal like mean

value, which needs to be preserved. The update step lifts the even sequence values

using the linear combination of the predicted odd sequence values so that the basic

properties of the original sequence is preserved [5]. The even sequence values s1

obtained as the result of equation 4.2 is equivalent to the sub-sampled low pass

version of the original sequence.

33

4.3 Method

4.2.1 Integer to Integer transform

It was observed that usually when wavelet transforms is performed on integer se-

quence it gives floating point coefficients. As per Calderbank [6] wavelet transform

which will map integers to integers can be build with the help of lifting structure.

This can be achieved by rounding off or updating the filter in each lifting step

before its addition or subtraction. The invert of the lifting steps can be produced

by following the exact reverse operation and flipping the signs.

4.3 Method

In recent days the wavelet analysis got a good recognition in research and devel-

opment area due to its characteristic of providing time and frequency information

simultaneously. As per research the retina of the eye splits an image in to several

frequency channels i.e. approximately one octave. In multi resolution decomposi-

tion, the image is divided into bands of equal bandwidth on a logarithmic scale.

There is lot of similarity between the signal processing of the human visual system

(HVS) and scaling decomposition of the wavelet transform, which can be achieved

by watermark embedding to the masking property or quantization method [7].

4.3.1 Description

The watermarks used in this approach:

1. Doctor’s identification code

2. Indexed watermark

3. Patient’s reference identification code

4. Patient’s diagnosis information

5. Patient’s treatment information

The listed watermarks used in this proposed watermarking scheme helps in ad-

dressing different issues and concerns in healthcare management system, Such as

confidentiality of medical data, recovering original image without any distortion,

data integrity, authentication and efficient data management.

34

4.3 Method

Confidentiality of medical data is achieved by embedding watermark using

Integer to Integer Discrete Wavelet Transform (IDWT), which confirms the im-

perceptibility property. This property ensures the embedded watermark will be

invisible to the normal human eye and the watermark can be extracted by the

one who knows the embedding and extraction algorithm applied in this system.

By applying Inverse IDWT at the receiver end original image can be recovered

without any distortion. Also the distortion to the ROI has already been avoided

by separating the ROI before embedding the watermark in to the medical image.

Medical data integrity is achieved by using fragile watermarking system, so any

manipulation on medical image data leads in distortion of embedded watermark.

For the authentication purpose the included watermarks such as doctor’s identifi-

cation code, patient’s identification code will ensures the entitled users can access

or modify the medical data. To provide efficient data management in this system

the indexed watermark is embedded which helps in retrieving the image for the

future reference if needed using database query.

The watermarks are inserted in different decomposition levels and sub-bands

depending on their type. They can be independently embedded and retrieved

without any intervention among them. By integrating this idea in to different

medical acquisition systems like Ultrasound, CT and MRI etc. This system can

be applied in different applications such as e-diagnosis or medical image sharing

through picture archiving and communication.

Selection of embedding coefficient

The Figure 4.3 illustrates the subband structure of a 4-level harr wavelet decompo-

sition of an original medical image, which is obtained after removing the ROI from

the host medical image. This decomposed image comprise of a coarse scale image

approximation at the highest decomposition level i.e. at 4th level, and it also

contains the twelve detail images corresponding to the horizontal (HL), vertical

(LH), and diagonal (HH) details at each of the 4-level.

The watermark holding the doctor’s identification is the most important for the

identification purpose and are of limited in length hence capacity is not very im-

portant. By considering this two points this doctor’s identification code containing

watermark is embedded in the fourth level, because more the decomposition level

more the robust watermark. On the other hand index watermark and patient’s

identification code requires more space than the doctor’s identification code since

they pass on many bits of additional information. The index watermark holds the

35

4.3 Method

decomp.png

Figure 4.3: Sub-band structure a of 4-level wavelet transform

information, which is used to retrieve the medical image. So the required capac-

ity for this watermark lies between the degrees of capacity intended for patient’s

identification code, patient’s diagnosis information, and treatment information.

Focusing on the above fact indexed watermark is embedded into third decompo-

sition level. The watermark contain patient’s identification is embedded into 2nd

decomposition level as it requires less space than both diagnosis information water-

mark and treatment watermark. The patient’s diagnosis information watermark

and patient’s treatment information watermark both requires the more capacity

so these fragile watermarks are embedded into 1st level. If any modification or

tampering to the embedded image occurs then the extracted watermark will be

totally different than embedded one.

In general horizontal and vertical sub-bands are used to embed the watermark,

as they have more or less same behavior in contrast to diagonal one. By embed-

ding the watermark into horizontal or vertical details coefficients results in less

distortion of image. Especially for the ultrasound images the energy of horizontal

details is more than compare to the vertical and diagonal details. This is due to

36

4.3 Method

Table 4.1: Energy of Approximation and Detail Images of Four LevelDWT

Sub-bands Level 1 Level 2 Level 3 Level 4Approximation 41.4514

Horizontal 5.4363 8.8305 14.8038 16.9878Vertical 4.5678 7.2816 9.0938 10.4323Diagonal 3.3392 5.6763 9.3863 12.5729

the elongation of ultrasound image mark spots in the horizontal direction [57].

For all other medical images the energy of horizontal and vertical details are ap-

proximately same. Table 4.1 shows the energy of the approximation and detail

images for a 4-level haar wavelet decomposition of an ultrasound test image shown

in Figure 4.4.

Figure 4.4: Ultrasound Image

From the table 4.1, it is clear that at the higher decomposition level corre-

sponding to the low frequency coefficients have the more energy than the energy

bat the lower decomposition level. Moreover the energy of horizontal details sub-

band is more than the energy of vertical and diagonal details sub-band. Hence

the watermarks are embedded into horizontal sub-bands in this system. As the

approximation sub-bands LL4 has most energy of the medical image and has the

huge amount of impact on the quality of medical image, it is not used for embed-

ding purpose to retain imperceptibility.

The energy of the approximation and detail images obtained by four-level DWT

can be calculated as:

ek =1

NkMk

∑i

∑j

|Ck (i, j)| (4.3)

Where k is the approximation and detail images at each of the decomposition

levels, Ck are the coefficients of the sub-band images, Mk and Nk are their corre-

sponding dimensions.

37

4.3 Method

4.3.2 Algorithm

In this algorithm the multiple watermarks embedding technique is used. Where,

depending on the quantization of selected coefficients the multiple watermarks

embedding procedure is used. This prevents any modification to the watermark

bits by granting integer changes in spatial domain of medical image. This can

be achieved by applying 4-levl haar wavelet transform to decompose the host

medical image. Moreover it gives the output as coefficients, which are in the

form of dyadic rational numbers. These coefficients denominators are in powers

of 2. The multiple of 2l (l is decomposition level) number adding or subtracting

to the produced coefficient value, assures that the inverse DWT provide integer

pixel values. Wavelet transform generally provides the coefficients which are real

numbers. By applying the quantization function it assigns the binary number to

every coefficient. This quantization function is defined as

Q(f) = 0, if

⌊(f − s

∆

)⌋is even (4.4)

Q(f) = 1, if

⌊(f − s

∆

)⌋is odd (4.5)

Where s is a user-defined offset for increased security, f is frequency coefficient

produced by haar wavelet transform and , the quantization parameter, is a positive

real number. Moreover ∆ is defined as ∆ =2l. The quantization procedure is

shown in Figure 4.5.

Figure 4.5: Quantization Procedure

As explain earlier, addition or subtraction of a multiple of 2l value to the haar

wavelet coefficient results in integer pixel values, after applying inverse DWT. Dur-

ing the embedding process the algorithm add or subtract an appropriate constant

to the haar coefficient chosen for watermark casting.

The algorithm for embedding multiple watermarks is explained below:

Step 1: Separate the ROI region from the host medical data using GUI based

38

4.3 Method

mouse clicking approach. Which results in image of RONI region, name it as orig-

inal medical image.

Step 2: Save the removed ROI from medical image.

Step 3: The multiple watermarks to be embed into a original image is generated

by reading the patient’s information file from text document, and converting it

into binary.

Step 4: Apply the 4-level Haar-lifting wavelet transform to original medical im-

age, to obtained a gross image approximation at the lowest resolution level and a

sequence of detail images corresponding to the horizontal, vertical, and diagonal

details at each of the four decomposition levels.

Step 5: On each decomposition level the watermark bit wi is embedded into

the key determined coefficient f, which is obtained by applying wavelet transform

according to the following condition:

1. If Q(f) = wi, the coefficient is not modified

2. Otherwise, the coefficient is modified so that Q(f) = wi, using the following

equation:

f = f + ∆, if f ≤ 0 (4.6)

f = f −∆, if f > 0 (4.7)

Step 6: The pre watermarked image is produced by performing the corresponding

four level inverse wavelet transform.

Step 7: The resultant watermarked image is obtained by combining the saved

ROI with the pre watermarked image.

The watermark extraction process is similar to that of embedding one except

that at the receiving end extractor should have the knowledge of location of the

embedded watermark. This can achieve by the key-based embedding and detec-

tion. With this type of method access to the watermark by unauthorized users is

prevented. The algorithm for extraction process to recover the host medical image

is explained below.

Step 1: Remove the ROI region from the received watermarked image with the

help of Xmax, Xmin, Ymin and Ymax parameter provided with watermarked im-

age.

Step 2: Apply the 4-level lifting-haar wavelet transform to the image which is

created from step 1, which results in a image approximation at level four and

sequence of images corresponding to the horizontal, vertical, and diagonal details

39

4.3 Method

at each of the four decomposition levels.

Step 3: Identify the location of watermark by key-based detection.

Step 4: Extract the watermarks by applying quantization function defined in

equation 4.4 and 4.5, which recovers the original coefficient. Convert the ex-

tracted binary watermark to text watermark.

Step 5: The pre output image is obtained by applying inverse 4-level haar wavelet

transform.

Step 6: combine the separated ROI region to the pre output image to get the

original host medical image.

40

Chapter 5

Experiments and Results

The proposed system has been applied against different type of medical image

such as, CT scan, MRI, X-Ray and Ultrasound. We have tested the system over

different size of medical images like 320 X 256, 384 X 384, and 512 X 512.

The applied watermark was consists of in

1. Doctor’s identity: G123468

2. Indexing for database: 321-123.1

3. Patient’s identification: sonika c rathi.190.85.04567851

4. Diagnosis Information: light.sugar healthy extra.spicy no.fats 12189.75.1

5. Treatment applied to the patient: painkiller.hgkkfgjklfd abcdefmglkh bkjdhflkds.yeio

The results after applying the system against CT scan, MRI, X-Ray and Ultra-

sound are shown below:

5.1 The experiments and results of the system

without attacks

5.1.1 CT Scan Images

The embedded and extracted watermark values are shown in Figure 5.4, with

PSNR value (p) and MSE (d).

The system is applied on different CT scan images considering their image

size and noted corresponding results, which are shown in table 5.1. The table

shows the PSNR value for ROI extracterd from host image and ROI extracted

41

5.1 The experiments and results of the system without attacks

Figure 5.1: Segmenatted ROI of host image

Figure 5.2: (a) The original host CT scan image, (b)Roi removed im-age,(c)Emebedded image without ROI, (d)Final embedded image with ROI

Figure 5.3: Recovered original image

from watermarked image. As the corner pixel values of ROI image is changed the

PSNR is not ∞ but there correlation is approximately 1. So, the selected ROI

should be large enough to not compromise with the diagnosis value. The table

also provides the PSNR value for embedded image and original image and there

respective mean square difference.

42


Figure 5.4: Embedded and extracted watermark values without any attacks

Table 5.1: Results of CT scan imagesSize MSE PSNR PSNR extracted ROI Correlation between the ROI

320 X 256 6.81 39.83 38.69 0.9963384 X 384 4.00 42.23 43.20 0.9994512 X 512 2.11 44.71 45.12 0.9998

5.1.2 MRI Images

The embedded and extracted watermark on the MRI image shown in Figure 5.5

(a), are given in Figure 5.6.

The results after applying the system on different size of MRI images are shown

in table 5.2.

Table 5.2: Results of MRI imagesSize MSE PSNR PSNR extracted ROI Correlation between the ROI

320 X 256 6.86 39.40 37.24 0.9962384 X 384 3.84 42.28 41.20 0.9993512 X 512 2.22 44.70 44.12 0.9998

43


Figure 5.5: (a) The original host MRI image, (b)ROI image of host image,(c)Roiremoved image (c)Emebedded image without ROI, (d)Final embedded image withROI

, (e)Recovered image

Figure 5.6: Embedded and extracted watermark values without any attacks forMRI image

5.1.3 X-Ray Images

The embedded and extracted watermark on the X-Ray image shown in Figure 5.7

(a), are given in Figure 5.8.

44


Figure 5.7: (a) The original host X-Ray image, (b)ROI image of host image,(c)Roiremoved image (c)Emebedded image without ROI, (d)Final embedded image withROI


Figure 5.8: Embedded and extracted watermark values without any attacks forX-Ray image

The results after applying the system against different size of X-Ray images

are shown in table 5.3.

45

5.2 Embedded and extracted watermark with attacks

Table 5.3: Results of X-Ray imagesSize MSE PSNR

320 X 256 6.93 39.60384 X 384 3.92 42.18512 X 512 2.16 44.63

5.1.4 Ultrasound Images

Figure 5.9: (a) The original host Ultrasound image, (b)ROI image of host im-age,(c)Roi removed image (c)Emebedded image without ROI, (d)Final embeddedimage with ROI


The embedded and extracted watermark on the Ultrasound image shown in

Figure 5.9 (a), are given in Figure 5.10.

The results after applying the system against different size of Ultrasound im-

ages are shown in table 5.4.

5.2 Embedded and extracted watermark with at-

tacks

The embedded and extracted watermark after applying different attacks on the

watermarked images are shown in this section. The attacks applied on the water-

46


Figure 5.10: Embedded and extracted watermark values without any attacks forUltrasound image

Table 5.4: Results of Ultrasound imagesSize MSE PSNR

320 X 256 6.93 39.60384 X 384 3.92 42.18512 X 512 2.16 44.63

marked medical images are:

• Slat and pepper noise attack

• Cropping attack

• Histogram equalization

• Sharpning attack

• Sampling attack

• JPEG compression attack

The following figures shows the attacked watermark medical image and em-

bedded, extracted watermark of CT scan, MRI, X-Ray and Ultrasound images:

47


Figure 5.11: (a) The original watermarked CT scan image, (b)The image aftersharpning attack with 0.02 factor

The extracted watermark from the attacked CT scan image is shown in Figure

5.12. The sharpning attack with 0.02 factor is applied on watermarked CT scan

image, shown in Figure 5.11 (a).

Figure 5.12: Embedded and extracted watermark values with sharpning attck(0.02 factor) image

The Figure 5.13 shows the original watermarked MRI image and the histogram

attacked watermarked image. The embedded and extracted watermark values

48


after histogram attack are given in Figure 5.14.

Figure 5.13: (a) The original watermarked MRI image, (b)The image after His-togram attack

Figure 5.14: Embedded and extracted watermark values after histogram attackon image

As, the algorithm implemented is fragile system, after even the 10% of jpeg

compression to the X-Ray image, the extracted watermark from attacked image is

totally distorted. This distortion of extracted watermark is shown in Figure 5.16,

the original watermarked image and attacked image is shown in Figure 5.15.

49


Figure 5.15: (a) The original watermarked X-Ray image, (b)The image after 10%JEPG compression attack

Figure 5.16: Embedded and extracted watermark values after JEPG Compressionattack on X-Ray image

The Figure 5.17 shows the original watermarked image and attacked water-

marked image of ultrasound image. Here the Down and Up sampling attack is

applied on watermarked ultrasound image. The attack is applied with 1 factor of

50


down sampling and 1 factor of up sampling on the watermarked image. The Fig-

ure 5.17 clearly shows that both the images are look like same, that is by normal

human eye the difference between the two image is not visible. However the ex-

tracted watermark from the attacked image is totally different than the embedded

one. The embedded and extracted watermark values are shown in Figure 5.18

Figure 5.17: (a) The original watermarked Ultrasound image, (b)The image afterup and down sampling attack

51


Figure 5.18: Embedded and extracted watermark values after down and up sam-pling attack on Ultrasound image

52

Chapter 6

Conclusion and Future Work

6.1 Conclusion

There exist various medical image watermarking algorithms which provide the

confidentiality of medical data, recovering original image without any distortion,

data integrity, authentication and efficient data management. Also the different

segmentation algorithms are in place, which vary for the types of medical images

such as MRI, CT scan, X-ray and Ultrasounds etc.

Here the proposed system used an algorithm to separate ROI from the host

medical image that will be applicable for all types of medical images. Separated

ROI can be stored with xmin, xmax, ymin, and ymax value so that at the end of

embedding process before transmitting watermarked image, the segmented ROI

can be attached with watermarked image. And the ROI region which is considered

as a critical data and used as a reference by the physician for the treatment will

be safe.

6.2 Future Work

Proposed system uses DWT approach for embedding the watermark, instead of

DWT use of Complex Wavelet Transform (CWT) will make the system more ro-

bust and secure.

The current proposed system can further be extended to provide more secured

system. This can be done by encrypting the watermark using secret key, before

embedding it in to medical images. Having the automated tool for separating the

53

6.2 Future Work

ROI from medical image will provide faster system and more accurate system,

which will be easier for end user.

The watermark before embedding can be compressed and then embedded. This

will lead to more secured system. Also, it will take more effort to break the system.

54

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